Single Large Payload or Multiple Small Payloads?

When designing a UI with multiple portlets/widgets, choosing between using one large payload or multiple smaller ones for each widget depends on various factors such as performance, maintainability, and user experience. Let’s look at the pros and cons of both approaches:

1. Single Large Payload (One payload for all widgets)

Pros:

• Reduced Number of Requests: Since all data is fetched in one request, you avoid multiple round-trips to the server, reducing the overhead associated with multiple network calls like latency, DNS lookups, and handshakes.
• Better for Bulk Operations: If all widgets depend on similar or overlapping data, fetching everything at once can ensure that the UI is consistent and avoids the need for multiple redundant data-fetching operations.
• Less Coordination Between Components: You don’t need to worry about ensuring that different parts of the UI are loaded at different times or in a specific order, as all components receive the data at once.

Cons:

• Over-fetching Data: Some widgets may not need all the data that is retrieved. This can lead to unnecessary data transfer, increasing payload size and wasting bandwidth.
• Slower Initial Load: If the data set is large, it could take longer to download, potentially slowing down the initial page load and delaying the rendering of any part of the UI.
• Harder to Cache: Caching strategies may become more difficult, as the entire payload may need to be invalidated even if only one widget’s data changes, leading to inefficiencies.
• Complex Error Handling: A failure in the large request can prevent the entire UI from rendering or updating, leading to a poor user experience.

2. Multiple Smaller Payloads (One payload per widget)

Pros:

• Optimized Data Transfer: Each widget fetches only the data it needs, potentially reducing the overall amount of data transferred. This can be more efficient, especially when data requirements vary significantly between widgets.
• Faster Perceived Performance: Widgets can load independently and display as soon as their respective data arrives, improving the perceived speed of the UI. This approach allows for more progressive rendering, giving users access to portions of the interface without waiting for everything to load.
• Easier Caching: Each widget’s data can be cached independently, making it easier to handle partial updates. If one part of the data changes, only that widget’s cache needs to be refreshed.
• Graceful Degradation: If one widget’s data fails to load, it only affects that widget rather than the entire UI. This enhances the robustness of the application by allowing partial failures.

Cons:

• Increased Number of Requests: Multiple smaller requests could increase the total number of network calls, leading to overhead in terms of connection setup, latency, and resource contention. In cases of limited bandwidth or high latency, this can become a bottleneck.
• Potential for Jank: If requests for different widgets are not optimized or asynchronous, users might notice a staggered or janky load experience, where certain parts of the UI appear later than others. This could affect user perception if not handled carefully.
• Dependency Complexity: Managing dependencies between widgets becomes more complex. If some widgets depend on data from other widgets, there could be potential delays or inconsistent states if not well-managed.
• Increased Client-Side Logic: The client-side logic needs to handle the orchestration of multiple requests, including handling failures, retries, and rendering states for individual components. This can add complexity to the front-end code.

Which approach is better?

The best approach depends on the nature of your application and the user experience you want to deliver. Here’s a general guide:

• Use a Single Large Payload if:
• You have significant data overlap between widgets.
• All widgets need to load simultaneously or rely on similar data.
• The total payload size is manageable and doesn’t degrade performance.
• Your system is sensitive to the number of network requests and you want to minimize them.
• Use Multiple Small Payloads if:
• Widgets are independent of each other and don’t share much data.
• You want a faster initial load experience, with widgets loading progressively.
• You need better caching or error isolation.
• Data requirements vary greatly between widgets.

Hybrid Approach

Often, the most optimal solution is a hybrid approach. For example, you could:

• Group related widgets into a few larger payloads.
• Load critical widgets (above the fold) in a single request to speed up the initial render, while loading non-essential widgets lazily with smaller requests.
• Use intelligent batching or GraphQL to reduce over-fetching while still minimizing the number of network calls.

This hybrid solution gives you flexibility in terms of performance, maintainability, and user experience.

Size Matters

There is no universal “max size” for a payload as it depends on various factors, including the type of payload (e.g., HTTP request, API payload), the protocol being used, and specific limits imposed by browsers, web servers, or APIs. However, here are some general guidelines and practical considerations:

1. Browsers (GET and POST Requests)

• GET Request:
  • URL Length Limit: The maximum size of a GET request payload is determined by the URL length limit, which is generally 2,048 characters in most browsers. However, this varies slightly by browser and server.
  • GET requests should be used for retrieving data, and the payload (in terms of query parameters) should be small. If you exceed this limit, the request may fail.
• POST Request:
  • There is no strict limit for POST requests in terms of body payload size, but limits are often set by the server or network infrastructure (e.g., reverse proxies).
  • For most web applications, a safe guideline is keeping payloads under 2 MB. Anything larger could cause performance issues or be rejected by the server.

2. API Payloads (REST/GraphQL)

• REST APIs:
  • Generally, most servers allow payloads up to 2–10 MB by default for REST API POST requests. This depends on server configuration (e.g., Nginx, Apache).
  • Large REST payloads (e.g., over 10 MB) can cause performance bottlenecks and may be subject to stricter rate limits or timeouts.
• GraphQL:
  • Similar to REST, GraphQL payloads are generally treated like POST requests. The limits are usually imposed by the server.
  • GraphQL queries tend to be more optimized for smaller payloads since they allow you to specify only the fields you need.

3. Web Servers

• Nginx: By default, the client_max_body_size setting in Nginx is 1 MB, but this can be configured to allow larger payloads.
• Apache: The default limit for the body of POST requests is 2 GB, but practical limits are often set lower for performance and security reasons.

4. Mobile Networks and Latency

• While payloads of up to 10 MB might work over Wi-Fi or fast connections, mobile networks and high-latency connections often perform poorly with large payloads. It’s common to limit payload sizes to under 500 KB for optimal performance on slower networks.

5. Serverless Functions (AWS Lambda, Google Cloud Functions)

• AWS Lambda: AWS imposes a limit of 6 MB for synchronous payloads (request/response size) and 256 KB for asynchronous invocations (e.g., triggered by events).
• Google Cloud Functions: A payload limit of 10 MB for HTTP requests.

6. File Uploads (multipart/form-data)

• For file uploads, the practical limit depends on the form of data being sent (e.g., multipart) and the server’s configuration. Typically, most web servers handle up to 50–100 MB for file uploads, but larger sizes (e.g., video files) may require special configuration.

7. WebSockets and Long-Polling

• WebSockets allow continuous data transmission without a traditional request-response model, making payload size limits less restrictive. However, some WebSocket implementations limit message sizes to 1–10 MB.

Summary of Common Payload Limits:

• Browsers (GET Requests): ~2 KB (URL length)
• Browsers (POST Requests): ~2 MB (depending on server config)
• REST APIs: 2–10 MB (default server limits)
• GraphQL: Similar to REST, often 2–10 MB
• Nginx/Apache default: 1 MB for Nginx, 2 GB for Apache (configurable)
• AWS Lambda: 6 MB synchronous, 256 KB asynchronous
• Google Cloud Functions: 10 MB

Best Practices:

• Keep payloads as small as possible for faster performance, especially in high-latency environments.
• If you need to send large data (like file uploads), consider chunking or streaming the data to avoid hitting payload size limits.
• Use pagination or lazy loading techniques when retrieving large datasets from the server to avoid large payloads.